

import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin

class     __snake_case    (				__lowerCAmelCase			,	unittest.TestCase  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							PhobertTokenizer
       __SCREAMING_SNAKE_CASE				    =							False


       def 			a		(		self   )      ->							Tuple:






              """simple docstring"""


              super().setUp()

              # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
              __snake_case       	=							["""T@@""", """i""", """I""", """R@@""", """r""", """e@@"""]
              __snake_case       	=							dict(zip(lowerCamelCase_      , range(len(lowerCamelCase_   )   )   )   )
              __snake_case       	=							["""#version: 0.2""", """l à</w>"""]
              __snake_case       	=							{"""unk_token""": """<unk>"""}

              __snake_case       	=							os.path.join(self.tmpdirname      , VOCAB_FILES_NAMES["""vocab_file"""]   )
              __snake_case       	=							os.path.join(self.tmpdirname      , VOCAB_FILES_NAMES["""merges_file"""]   )

              with open(self.vocab_file      , """w"""      , encoding="""utf-8"""   ) as fp:
                     for token in vocab_tokens:
                            fp.write(F'{token} {vocab_tokens[token]}\n'   )
              with open(self.merges_file      , """w"""      , encoding="""utf-8"""   ) as fp:
                     fp.write("""\n""".join(lowerCamelCase_   )   )


       def 			a		(		self      , **_UpperCamelCase   )      ->							Dict:






              """simple docstring"""


              kwargs.update(self.special_tokens_map   )
              return PhobertTokenizer.from_pretrained(self.tmpdirname      , **lowerCamelCase_   )


       def 			a		(		self      , _UpperCamelCase   )      ->							Tuple:






              """simple docstring"""


              __snake_case       	=							"""Tôi là VinAI Research"""
              __snake_case       	=							"""T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"""
              return input_text, output_text






       def 			a		(		self   )      ->							str:






              """simple docstring"""


              __snake_case       	=							PhobertTokenizer(self.vocab_file      , self.merges_file      , **self.special_tokens_map   )
              __snake_case       	=							"""Tôi là VinAI Research"""
              __snake_case       	=							"""T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h""".split()
              __snake_case       	=							tokenizer.tokenize(lowerCamelCase_   )
              print(lowerCamelCase_   )
              self.assertListEqual(lowerCamelCase_      , lowerCamelCase_   )

              __snake_case       	=							tokens + [tokenizer.unk_token]

              __snake_case       	=							[4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
              self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_   )      , lowerCamelCase_   )


# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

_SCREAMING_SNAKE_CASE 		=      """tiny-wmt19-en-ru"""

# Build

# borrowed from a test
_SCREAMING_SNAKE_CASE 		=      [
    """l""",
    """o""",
    """w""",
    """e""",
    """r""",
    """s""",
    """t""",
    """i""",
    """d""",
    """n""",
    """w</w>""",
    """r</w>""",
    """t</w>""",
    """lo""",
    """low""",
    """er</w>""",
    """low</w>""",
    """lowest</w>""",
    """newer</w>""",
    """wider</w>""",
    """<unk>""",
]
_SCREAMING_SNAKE_CASE 		=      dict(zip(vocab, range(len(vocab))))
_SCREAMING_SNAKE_CASE 		=      ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

with tempfile.TemporaryDirectory() as tmpdirname:
     _SCREAMING_SNAKE_CASE 		=      Path(tmpdirname)
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""src_vocab_file"""]
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""tgt_vocab_file"""]
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""merges_file"""]
     with open(src_vocab_file, """w""") as fp:
          fp.write(json.dumps(vocab_tokens))
     with open(tgt_vocab_file, """w""") as fp:
          fp.write(json.dumps(vocab_tokens))
     with open(merges_file, """w""") as fp:
          fp.write("""\n""".join(merges))

     _SCREAMING_SNAKE_CASE 		=      FSMTTokenizer(
         langs=["""en""", """ru"""],
         src_vocab_size=len(vocab),
         tgt_vocab_size=len(vocab),
         src_vocab_file=src_vocab_file,
         tgt_vocab_file=tgt_vocab_file,
         merges_file=merges_file,
     )

_SCREAMING_SNAKE_CASE 		=      FSMTConfig(
    langs=["""ru""", """en"""],
    src_vocab_size=1_0_0_0,
    tgt_vocab_size=1_0_0_0,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

_SCREAMING_SNAKE_CASE 		=      FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
_SCREAMING_SNAKE_CASE 		=      tokenizer(["""Making tiny model"""], return_tensors="""pt""")
_SCREAMING_SNAKE_CASE 		=      tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-ru
